Abstract
Comparative study is conducted by applying deep and shallow network on energy data for non-intrusive load monitoring (NILM) applications. From the total energy consumption, various appliance consumption can be disaggregated using shallow and deep networks. The study highlights the effective method for energy disaggregation from the total energy consumption using algorithms rather than the expensive method of separately metering individual loads. Shallow algorithm support vector regression and deep learning algorithms such as deep neural network (DNN) and long short term memory (LSTM) are used in this study and their performance is evaluated.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
